| Summary: | 碩士 === 國立成功大學 === 電機工程學系 === 102 === Due to high uncertainty and fast fluctuations of wind speed, in the power system with high penetration of wind power generation, spinning reserve scheduling and power dispatching are two major problems encountered. To achieve the purposes, wind power forecasting is, therefore, a prerequisite for the integration of a large-scale wind generation farm in the electric power system.
This thesis aims to present a short-term wind power forecasting method with numerical weather prediction (NWP) data integrated. The forecasts are up to 4-hr ahead with a 15-min interval, making the wind power forecasts suitable for addressing the spinning reserve scheduling and power dispatching problems. The proposed forecasting method integrates probability distribution analysis, finite-mixture model, multi-resolution analysis (MRA), radial basis neural networks (RBFNNs), fuzzy inference (FI), and near real-time forecasting approaches.
Tested on the historical one-year wind-speed data, the numerical results obtained show that the forecasting accuracy of the wind power in terms of monthly average mean relative error (MRE) and relative error root mean square error (RMSE) for a 2MW wind-generation system is 8.52% and 287.28 kW, respectively. The performance obtained is obviously better than the compared conventional neural networks (NNs) and MRA-NNs methods in the thesis.
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